Unraveling spatial cellular pattern by computational tissue shuffling

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Choroid plexuses carry nodal-like cilia that undergo axoneme regression from early adult stage

Choroid plexuses (ChPs) produce cerebrospinal fluid and sense non-cell-autonomous stimuli to control the homeostasis of the central nervous system. They are mainly composed of epithelial multiciliated cells, whose development and function are still controversial. We have thus characterized the stepwise order of mammalian ChP epithelia cilia formation using a combination of super-resolution-microscopy approaches and mouse genetics. We show that ChP ciliated cells are built embryonically on a treadmill of spatiotemporally regulated events, starting with atypical centriole amplification and ending with the construction of nodal-like 9+0 cilia, characterized by both primary and motile features. ChP cilia undergo axoneme resorption at early postnatal stages through a microtubule destabilization process controlled by the microtubule-severing enzyme spastin and mitigated by polyglutamylation levels. Notably, this phenotype is preserved in humans, suggesting a conserved ciliary resorption mechanism in mammals.

Multiplex imaging in immuno-oncology

Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.